Perfusion imaging based on dynamic contrast enhanced (DCE) MRI is a widely used technique for grading tumors, especially for brain tumors (gliomas) due to their inherent inaccessibility. Several studies have shown a good correlation between histopathological glioma grade and blood volume (BV) derived from perfusion imaging.
Traditionally, an experienced neuroradiologist identifies the tumor region in relevant slices, and the tumor can be graded. however, there is a movement towards computer aided diagnostics (CAD) within tumor grading, and automated methods for segmenting (identifying) tumors from MR images exists, as well as automated methods for grading the identified tumors in BV maps (hot-spot or histogram methods).
Blood volume maps (or similar perfusion related maps) used in automated tumor identification and grading are meant to determine the volume of blood in a region of tissue. The blood volume is used to evaluate the micro-vascular density or vascularity, in other words, the density of small blood vessels (capillaries) in a tissue region. BV map is preferably obtained by perfusion imaging whereby images are acquired before, during and after injection of a contrast agent. Due to relatively small voxel sizes (typically tens of mm2) of the perfusion imaging technique, large vessels in the region could result in a misleading shift of the BV frequency distribution towards higher BV values.
Therefore, inclusion of BV values from vessels having dimensions of the order of or larger than the typical spatial resolution of the applied imaging technique is a confounding factor in the grading, as these are not separable from values from malicious tissue in the automated tumor segmentation. BV values from vessels are therefore sought to be excluded from the regions whose BV values are used in the data analysis. Thus, reliable data may only be obtained by, prior to the automated grading, having a neuroradiologist manually exclude larger vessels from the BV maps based on anatomical knowledge and experience.